研究人员开发了一种名为结构化语义数据增强（SSDAU）的新数据增强技术，以提高联合实体和关系抽取（JERE）模型的泛化能力。现有方法通常无法保留语义结构，导致增强数据无效。SSDAU通过基于实体标签对文本进行分段，通过上下文感知捕获语义特征，并使用BERTTopic重构实体同时确保主题一致性来解决此问题。 AI
影响 这种新的增强方法有望提高信息抽取任务中AI模型的鲁棒性和泛化能力。
排序理由 该集群包含一篇详细介绍AI数据增强新方法的学术论文。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
arXiv:2605.23440v1 Announce Type: cross Abstract: Joint Entity and Relation Extraction (JERE) is highly susceptible to weak generalization due to low-quality training data. Data augmentation is a common strategy to enhance model generalization across different domains. However, e…
Joint Entity and Relation Extraction (JERE) is highly susceptible to weak generalization due to low-quality training data. Data augmentation is a common strategy to enhance model generalization across different domains. However, existing data augmentation methods often overlook t…